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Record W4388539826 · doi:10.1111/vec.13349

Acute kidney injury in dogs following ingestion of cream of tartar and tamarinds and the connection to tartaric acid as the proposed toxic principle in grapes and raisins

2023· letter· en· W4388539826 on OpenAlexaboutno aff
Nicola Bates, Zoe Tizzard, John N. Edwards

Bibliographic record

VenueJournal of Veterinary Emergency and Critical Care · 2023
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVenomous Animal Envenomation and Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBATESMedicineVeterinary medicineLibrary scienceEngineeringComputer science

Abstract

fetched live from OpenAlex

Dear Editor, We are very interested in the report of cases of tartaric acid and tartrate poisoning from tamarinds and cream of tartar contributed by Wegenast and colleagues,1 which support the theory that this is the cause of acute kidney injury (AKI) associated with grape and raisin poisoning. We would like to contribute additional supporting data. At the Veterinary Poisons Information Service (VPIS), UK, we have no reported cases involving tamarind but do have cases involving cream of tartar. One dog, an adult Labrador, developed vomiting, inappetence, and AKI after ingestion of an unknown amount of cream of tartar. The dog was euthanized, and results of a urine sample received after euthanasia was positive for Haemophilus haemoglobinophilus, which may have been a confounder. Although dogs appear to be particularly susceptible to AKI from tartrates and tartaric acid,1 cases have also been reported in people, although 10–20 g of sodium tartrate was used previously as a laxative in people. Acute tubular necrosis has been reported after ingestion of tartaric acid2 and fatal tubular nephropathy occurred in an adult male following accidental ingestion of 30 g of tartaric acid.3 Wegenast and colleagues mention that tartaric acid delays gastric emptying, which may explain prolonged retention of dried vine fruits in the stomach. In addition, raisins are hygroscopic and can double or triple in volume on contact with water, and phytobezoars involving raisins have been reported in people.4, 5 Wegenast and colleagues suggest that cooked grapes and raisins are less likely to be implicated in cases of AKI due to thermal decomposition of tartaric acid. In a study comparing thermal decomposition of pure tartaric acid and that in waste samples from the grape juice and wine industries, the peak decomposition of pure tartaric acid occurred at 208°C but was closer to 300°C for the waste products.6 We have fatal cases of AKI in dogs after ingestion of Christmas pudding and fruit cake (including Christmas cake), all of which are rich in dried vine fruits. These baked goods are cooked for a prolonged period, but at a relatively low temperature (usually around 120–140°C), so it is difficult to know if the poor outcome was due to the dose ingested or the low cooking temperature resulting in incomplete decomposition of tartaric acid. Late presentation was a characteristic of these cases and is well recognized as a factor contributing to poor outcome in cases of tartaric acid-containing foods. Interestingly, we have no fatal cases involving other baked goods containing dried vine fruits such as scones or mince pies that are typically cooked for a shorter time at higher temperatures, but generally not above 200°C. We still have a great deal to learn about grape and raisin toxicosis in dogs, but the recognition of tartaric acid as the probable causative agent is a breakthrough. Our data support the findings of Wegenast and colleagues, but data are still lacking, and current treatment advice remains unchanged. Now we need to understand the effect of cooking on vine fruits and other factors that influence the outcome in dogs that ingest tartaric acid-containing foods. We would welcome further well-described case reports, particularly with information on doses ingested. Respectfully,

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.788
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.320
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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